VS298: Unsolved Problems in Vision

This seminar is about unsolved problems in vision.

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Instructor: Bruno Olshausen

Enrollment information:

VS 298 (section 4), 2 units
CCN: 66489

Meeting time and place:

Monday 6-8, Evans 560

Email list:

nss2014@lists.berkeley.edu subscribe

Readings:

Books and review articles:

• Natural Image Statistics by Hyvarinen, Hurri & Hoyer
• Olshausen BA & Lewicki MS (2013) What natural scene statistics can tell us about cortical representation. In: The Cognitive Neurosciences V. paper
• Geisler WS (2008) Visual perception and the statistical properties of natural scenes. Annual Review of Psychology paper

Weekly schedule:

Date	Topic/Reading	Presenter
Feb. 3	Redundancy reduction, whitening, and power spectrum of natural images Barlow (1961): Theory of redundancy reduction paper Atick (1992): Theory of whitening paper Field (1987): 1/f2 power spectrum and sparse coding paper Additional reading: Attneave (1954) - 'Some informational aspects of visual perception' paper Laughlin (1981) - Histogram equalization of contrast response paper Srinivasan (1982) - 'Predictive coding: a fresh view of inhibition in the retina' paper Switkes (1978) - Power spectrum of carpentered environments paper Ruderman (1997) - Why are images 1/f2? paper Torralba & Oliva (2003) - Power spectrum of natural image categories paper	Anthony DiFranco Dylan Paiton Michael Levy
Feb. 10	Whitening in time and color; Robust coding Dong & Atick (1995): spatiotemporal power spectrum of natural movies paper Ruderman (1998): statistics of cone responses paper Karklin & Simoncelli (2012): noisy population coding of natural images paper Additional reading: Dong & Atick (1995) - spatiotemporal decorrelation using lagged and non-lagged cells paper Doi & Lewicki (2007) - A theory of retinal population coding paper	Chayut Thanapirom Michael Levy Yubei Chen
Feb. 17	** Holiday **
Feb. 24	Higher-order statistics and sensory coding Barlow (1972): Sparse coding paper Field (1994): What is the goal of sensory coding? paper Bell & Sejnowski (1995): Independent component analysis. paper Additional reading: Redlich (1993): Redundancy Reduction as a Strategy for Unsupervised Learning. paper Baddeley (1996): Searching for filter with 'interesting' output distributions: An uninteresting direction to explore? paper O'regan & Noe (2001): A sensorimotor account of vision and visual consciousness paper	Karl Zipser Michael Levy Mayur Mudigonda
March 3	ICA and sparse coding of natural images Bell & Sejnowski (1997): ICA of natural images paper Olshausen & Field (1997): Sparse coding of natural images paper van Hateren & Ruderman (1998), Olshausen (2003): ICA/sparse coding of natural video paper1, paper2 Additional reading: Olshausen & Field (1996): simpler explanation of sparse coding paper	Mayur Mudigonda Zayd Enam Georgios Exarchakis
March 11 **Tuesday**	Statistics of natural sound and auditory coding Clark & Voss: '1/f noise and music' paper Smith & Lewicki: sparse coding of natural sound paper Klein/Deweese: ICA/sparse coding of spectrograms paper1, paper2	Tyler Lee Yubei Chen TBD
March 17	Higher-order group structure Geisler: contour statistics paper Hyvarinen: subspace ICA/topgraphic ICA paper1, paper2 Lyu & Simoncelli: radial Gaussianization paper Additional reading: Parent & Zucker (1989): Trace Inference, Curvature Consistency, and Curve Detection, paper Field et al. (1993): Contour Integration by the Human Visual System: Evidence for a Local “Association Field” paper Zetzsche et al. (1999): The atoms of vision: Cartesian or polar? paper Garrigues & Olshausen (2010): Group Sparse Coding with a Laplacian Scale Mixture Prior, paper	Chayut Thanapirom Guy Isely TBD
March 24	** Spring recess **
March 31	Energy-based models Hinton: Product of experts models, paper Osindero & Hinton: Product of Experts model of natural images, paper Roth & Black: Fields of experts, paper Additional reading: Hinton: Practical guide to training RBMs paper Teh et al: Energy-based models for sparse overcomplete representation, paper Zhu, Wu & Mumford: FRAME (Filters, random fields, and maximum entropy), paper	Evan Shelhamer Brian Cheung Chris Warner
April 7	Learning invariances through 'slow feature analysis' Foldiak/Wiskott: slow feature analysis, paper1, paper2 Hyvarinen: 'Bubbles' paper Berkes et al.: factorizing 'what' and 'where' from video, paper	Guy Isely Chayut Thanapirom Bharath Hariharan
April 14	Manifold and Lie group models Carlsson et al.: Klein bottle model of natural images, paper Culpepper & Olshausen: Learning manifold transport operators, paper Roweis & Saul: Local Linear Embedding, paper	Yubei Chen Bruno/Mayur James Arnemann
April 21	Hierarchical models Karklin & Lewicki (2003): density components, paper Shan & Cottrell: stacked ICA, paper Cadieu & Olshausen (2012): learning intermediate representations of form and motion, paper	Tyler Lee Brian Cheung Dylan Paiton
April 28	Deep network models Hinton & Salakhudinov (2006): stacked RBMs, paper Le et al. (2011): Unsupervised learning (Google brain, 'cat' neurons), paper Krishevsky et al. (2012): Supervised learning, ImageNet 1000 paper	TBD TBD Reza Abbasi-Asl
May 6 Note: Tuesday	Special topics Fergus (2013): visualizing what deep nets learn paper Schmidhuber: deep nets (paper), focusing on LOCOCODE (paper) Image compression with Hopfield networks	Shiry Ginosar Anthony DiFranco Chris Hillar
May 12	Special topics